To model the heterogeneous effects and find the producers for whom engagement received is most meaningful, we first run an A/B test because we need experimental variation for our causal inference ([1]}, [2]}). A/B tests are standard in the technology industry nowadays. In the A/B test a randomly selected set of producers are given more distribution, while a control set of producers are receiving no change in their content's ranking. Such A/B tests might have already been run in the past ([3]}), in which case we can use the already existing experiment. Once we have the experimental data collected over a reasonable period (eg a week), then we use a three-tree method to estimate the heterogeneous treatment effects (similar methods are laid out in [4]} and [5]}). All data collected has been aggregated and de-identified.
